def knn_string_build(self, digit_pos, bar, red_threshold=120):
knn_string = ''
char_width = self.layout.champ_detail.bar_char_width
for i in range(4):
start = max(0, digit_pos + (i * char_width))
end = min(start + char_width, len(bar[0]))
# Examine the digit area for red pixels greater than the threshold
# (indication of white text, since backgrounds are blue and green).
digit_subimg = bar[:, start:end]
thresh = cv2.inRange(digit_subimg, (0, 0, red_threshold), (255, 255, 255))
num_red = cv2.countNonZero(thresh)
if num_red < 5:
knn_char = ' '
else:
knn_char = self.knn_match(digit_subimg)
# geração de dados pra treino/teste
# cv2.imwrite("testdata/numbers/roi-%s.png" % self.counter, digit_subimg)
# self.counter += 1
if self.debug:
imprev(digit_subimg)
print(knn_char)
# imprev(digit_subimg)
knn_string += knn_char
knn_string = knn_string.strip()
try:
return int(knn_string)
except ValueError:
# TODO: We should use these as test cases to improve this algorithm
return -1
def slashfind(self, bar, slash_match_threshold=0.69, allow_sub=True):
slash_window = self.layout.champ_detail.bar_slash_window
bar = bar[:, slash_window[0]:slash_window[1]]
slash_matches = [ cv2.matchTemplate(bar, slash_temp, cv2.TM_CCOEFF_NORMED)[0] for slash_temp in self.slash_temps ]
best_matches = np.maximum.reduce(slash_matches)
slash_loc = np.where(best_matches == best_matches.max())[0]
if len(slash_loc) > 1:
# There was a tie for first. Use the first value.
if self.debug:
print 'found %d slashes' % len(slash_loc)
slash_loc = [slash_loc[0]]
if best_matches[slash_loc] < slash_match_threshold:
if self.debug:
print 'best match was below threshold:', best_matches[slash_loc]
if self.debug:
print 'slash_loc', slash_loc
imprev(bar)
return slash_loc[0] + slash_window[0]
def flatten(self, img):
return img.reshape((1,img.shape[0]*img.shape[1]))
def knn_match(self, digit, k_val=1):
# Convert the digit image into a float array of the flattened grayscale image
flat_grayscale = self.flatten(cv2.cvtColor(digit, cv2.COLOR_BGR2GRAY))
float_vals = np.float32(flat_grayscale)
# Try to find the closest match in our model to that float array
nearest_float = self.model.findNearest(float_vals, k=k_val)[0]
# We get an ascii value of the closest match. Return the actual character
return str(int(nearest_float))
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Given a frame image, prints the health and mana values and percents')
parser.add_argument('-i','--img', help='input image', required=True)
parser.add_argument('-v','--verbose', action='store_true', help='verbose debugging info')
args = parser.parse_args()
frame = cv2.imread(args.img)
layout = spatial_layout.layout_1280x1024_6_10()
hAndm = HealthManaCalc(layout, debug=args.verbose).get_health_and_mana(frame)
print("Health", hAndm['health'])
print("Mana", hAndm['mana'])
imprev(frame, label=args.img, mag=False, scale=1)